Optimal Auction for Resource Allocation in Wireless Virtualization: A Deep Learning Approach

Abstract

Wireless virtualization has become a key concept in future cellular networks which can provide multiple virtualized wireless networks for different mobile virtual network operators (MVNOs) over the same physical infrastructure. Resource allocation problem is a main challenge for wireless virtualization for which auction approaches have been widely used. However, for most existing auction-based allocation schemes, the objective is to maximize the social welfare (i.e., the sum of all valuations of winning bidders) due to its simplicity. While in reality, MVNOs are more interested in maximizing their own revenues. However, the revenue-maximization auction problem is much more complex since the price is unknown before calculation. In this paper, we give a first attempt for designing a revenueoptimal auction mechanism for resource allocation in wireless virtualization. Considering the complexity in revenue maximization, we apply the deep learning techniques. Specifically, we construct a multi-layer feed-forward neural network based on the analysis of optimal auction design. The neural network adopts users bids as the input and the allocation rule and conditional payment rule for the users as the output. The training set of this neural network is the users valuation profiles. The proposed auction mechanism possesses several satisfactory properties, e.g., individual rationality and incentive compatibility. Finally, simulation results demonstrate the effectiveness of the proposed scheme